{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Field manipulation and Saving the Radar Object to a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this notebook we will go over the basics of how to change the data inside a\n",
    "# radar file and save a new radar file. We will do this by creating a new field\n",
    "# that stores a reflectivity mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
      "## library for working with weather radar data. Py-ART is partly\n",
      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
      "## Science user facility.\n",
      "##\n",
      "## If you use this software to prepare a publication, please cite:\n",
      "##\n",
      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Import needed modules.\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pyart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the radar data.\n",
    "radar = pyart.io.read(\"sgpcsaprsurcmacI7.c0.20110520.095101.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the radar field into a variable.\n",
    "ref_field = radar.fields['reflectivity_horizontal']['data'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1 1 ... 1 1 1]\n",
      " [1 1 1 ... 1 1 1]\n",
      " [1 1 1 ... 1 1 1]\n",
      " ...\n",
      " [1 1 1 ... 1 1 1]\n",
      " [1 1 1 ... 1 1 1]\n",
      " [1 1 1 ... 1 1 1]]\n"
     ]
    }
   ],
   "source": [
    "# To create an array that is zero when the condition is false and one when it is true, we\n",
    "# can make use of the np.ma.where command.\n",
    "ref_gt_0 = np.ma.where(ref_field > 0, 1, 0)\n",
    "print(ref_gt_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To create a new field, we need to create a dictionary with keys containing the data,\n",
    "# the long name, the units, the fill value, and the standard name.\n",
    "mask_dict = {'data': ref_gt_0, 'units': '0 = Z < 0, 1 = Z >= 0', 'long_name': 'reflectivity_mask',\n",
    "             '_FillValue': ref_gt_0.fill_value, 'standard_name': 'reflectivity_mask'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding this field into the radar object using radar.add_field()\n",
    "radar.add_field('reflectivity_mask', mask_dict, replace_existing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sherm\\Anaconda3\\envs\\pyart_env\\lib\\site-packages\\cartopy\\mpl\\geoaxes.py:782: MatplotlibDeprecationWarning: Passing the minor parameter of set_xticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.\n",
      "  return super(GeoAxes, self).set_xticks(xticks, minor)\n",
      "C:\\Users\\sherm\\Anaconda3\\envs\\pyart_env\\lib\\site-packages\\cartopy\\mpl\\geoaxes.py:829: MatplotlibDeprecationWarning: Passing the minor parameter of set_yticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.\n",
      "  return super(GeoAxes, self).set_yticks(yticks, minor)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data using RadarMapDisplay\n",
    "plt.figure(figsize=[12, 8])\n",
    "projection = ccrs.LambertConformal(central_latitude=radar.latitude['data'][0],\n",
    "                                   central_longitude=radar.longitude['data'][0])\n",
    "display = pyart.graph.RadarMapDisplay(radar)\n",
    "display.plot_ppi_map('reflectivity_mask', projection=projection, cmap='coolwarm', vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Writing this radar object to a new file is as simple as using pyart.io.write_cfradial()\n",
    "pyart.io.write_cfradial('new_radar.nc', radar)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
